To approach the generalization problem of deep learning models, Liang et al. [1]} introduce a new notion of model complexity measure, the Fisher-Rao norm. Their study focuses on deep fully connected neural networks whose activation function \(\sigma \)  satisfies \(\sigma (z) = \sigma ^{\prime }(z)z\) . The model complexity represented by Fisher-Rao norm is given by
\(EMC(\mathcal {N}) = ||\theta ||^2_{fr} = \langle \theta , I(\theta )\theta \rangle \) 
